Excitable Rho dynamics control cell shape and motility by sequentially activating ERM proteins and actomyosin contractility

Migration of endothelial and many other cells requires spatiotemporal regulation of protrusive and contractile cytoskeletal rearrangements that drive local cell shape changes. Unexpectedly, the small GTPase Rho, a crucial regulator of cell movement, has been reported to be active in both local cell protrusions and retractions, raising the question of how Rho activity can coordinate cell migration. Here, we show that Rho activity is absent in local protrusions and active during retractions. During retractions, Rho rapidly activated ezrin-radixin-moesin proteins (ERMs) to increase actin-membrane attachment, and, with a delay, nonmuscle myosin 2 (NM2). Rho activity was excitable, with NM2 acting as a slow negative feedback regulator. Strikingly, inhibition of SLK/LOK kinases, through which Rho activates ERMs, caused elongated cell morphologies, impaired Rho-induced cell contractions, and reverted Rho-induced blebbing. Together, our study demonstrates that Rho activity drives retractions by sequentially enhancing ERM-mediated actin-membrane attachment for force transmission and NM2-dependent contractility.

For each time-lapse sequence, the per frame average ratio metric FRET activity was calculated and the means of 16 time-lapse sequences (FOV) per condition are shown (bold), along with the 95% confidence intervals (thin).Four biological replicates, n=16 FOV for each condition.
Figure S1.Localization/cell geometry-imposed bias on activity reported by the Rho FRET probes DORA-RhoA, RhoA2G, and RhoB sensor (A) (a) Side-by-side comparisons of FRET probe localizations (left) and FRET/CFP ratios (right) for DORA-RhoA, RhoA2G, and RhoB sensor.Scale bars, 25µm.Quantifications along the red arrows, manually drawn from the cell edge to the nucleus, are shown in (b).(b) Line plots from the images in (a) depicting normalized localization compared to normalized FRET/CFP ratio.Both were normalized to the maximum value along the line profiles.(c) Per-pixel correlation between localization intensity and FRET/CFP ratio of the two images in (a), with FRET/CFP plotted as a function of normalized localization signal.Pearson R correlation coefficients were calculated using MATLAB corrcoeff function, and line of best fit was plotted using MATLAB polyfit function.(B) Compiled Pearson R correlation coefficients for DORA-RhoA, RhoA2G, and RhoB sensor.Individual frames from time-lapse sequences from biological replicates were chosen at random and the correlation coefficient between normalized localization and FRET/CFP were calculated.DORA-RhoA: n= 69 frames from 23 cells, 2 independent trials.RhoA2G: n=75 frames from 25 cells, 2 biological replicates.RhoB sensor: n=75 frames from 25 cells, 3 biological replicates.The bolded circle in violin plot shows dataset median, and bolded black lines show 25th and 75th percentiles.***p<0.001,one-way ANOVA/Tukey-Kramer.

Figure S2 .
Figure S2.Spatiotemporal analysis of RhoB sensor and the localization-based Rho activity reporter dTomato-2xrGBD in the same cell (A) Schematic of localization-based Rho activity reporter, consisting of dTomato-2xrGBD and cytoplasmic miRFP680-NES.miRFP680-NES was co-expressed for normalization to cell geometry.(B) RhoB sensor.(C) dTomato-2xrGBD.(a) Rho activity as determined by RhoB sensor FRET/CFP and dTomato-2xrGBD/miRFP680 ratios.(b) (Left) Edge velocity maps and (right) spatiotemporal activity maps of cell in (a), measured within 1.95µm from the cell edge, from 62.5min time-lapse acquisitions.The same edge velocity maps are shown in (B) and (C) to facilitate comparison with spatiotemporal activity maps.(c) Cross-correlation between edge velocity and spatiotemporal activity maps.

Figure S3 .Figure S4 .
Figure S3.Edge velocity-Rho activity buildup analysis (A) Edge velocity and Rho sensor FRET/CFP spatiotemporal heatmaps with region of interest outlined by a black rectangle.Black rectangle is 15 coordinate windows wide, approx.8.33% of the cell perimeter, spanning a duration of 20 min.General region of analysis shown by white rectangle overlaid on cell outline on the right.Scale bar 10µm.(B) Time-lapse illustrating protrusion-retraction transition.Region of interest between coordinate windows 100 and 115 depicted by red ticks.Scale bar, 10µm.(C) Plot for the region of interest from (A) and (B) comparing average edge velocity to average RhoB sensor FRET/CFP activity per timepoint.Time=0 is placed at retraction onset, i.e., the transition from positive to negative edge velocity.RhoB sensor FRET/CFP per timepoint for varying window depths are plotted with different line styles.

Figure
Figure S5.Cross-correlation between RhoB sensor activity and mRuby3-MLC signal (A) Cross correlation between RhoB sensor FRET/CFP and mRuby3-MLC signal at an edge depth of 6.5µm.Pink lines represent results from individual cells, with the mean correlation bolded in black.n =27 cells from two biological replicates.(B) Average cross correlation between RhoB sensor FRET/CFP and mRuby3-MLC at window depths varying form 0.98 to 8.1µm.200s lag, where all traces reach their maximum, is indicated by a vertical line.n=27 cells from two biological replicates.(C) Comparison of average correlation-coefficient at a lag of 200 s for all edge depths tested, i.e., a quantification of the difference of the traces in (B) at 200 s lag.Mean +/-95% CI shown.n=27 cells from two biological replicates.*p<0.05,***p<0.001,one-way ANOVA/Tukey-Kramer.